Introduction to Python for Research

Instructor Biography

Course Description

Python is a general purpose programming language that includes a robust ecosystem of data science tools. These tools allow for fast, flexible, reusable, and reproducible data processes that make researchers more efficient and rigorous with existing study designs, while transparently scaling up to big data designs. This short course focuses on the foundational skills of identifying, collecting, and preparing data using Python. We will begin with an overview, emphasizing the specific skills that have a high return on investment for researchers. Then, we will walk through foundational Python skills for working with data. Using those skills, we will cover collecting data at scale using several techniques, including programmatic interfaces for obtaining data from WRDS, application programming interfaces (APIs) for a wide range of academic and popular data (e.g., The New York Times), web scraping for quantitative and text data, and computer-assisted manual data collections. From there, we will assemble and transform data to produce a ready-for-analysis dataset that is authoritatively documented in both code and comments, and which maintains those qualities through the variable additions, alternative measure construction, and robustness checks common to real projects. By the end of the course, you will have the skills—and many hands–on code examples—to conduct a rigorous and efficient pilot study, and to understand the work needed to scale it up. The course design does not assume any prior training, though reasonable spreadsheet skills and some familiarity with one of the commonly–used commercial statistical systems is helpful. In particular, no prior knowledge of Python is required, and we will cover a general introduction to Python in the beginning of the course content.

Short Course Introduction